Copertina

CardioBench: Do Echocardiography Foundation Models Generalize Beyond the Lab?

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Taratynova, Darya, Aly, Ahmed, Saeed, Numan, Yaqub, Mohammad
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Computer Vision and Pattern Recognition
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

TPA: Temporal Prompt Alignment for Fetal Congenital Heart Defect Classification
di: Taratynova, Darya, et al.
Pubblicazione: (2025)

BRIQA: Balanced Reweighting in Image Quality Assessment of Pediatric Brain MRI
di: Almsouti, Alya, et al.
Pubblicazione: (2025)

FETAL-GAUGE: A Benchmark for Assessing Vision-Language Models in Fetal Ultrasound
di: Alasmawi, Hussain, et al.
Pubblicazione: (2025)

CoReEcho: Continuous Representation Learning for 2D+time Echocardiography Analysis
di: Maani, Fadillah Adamsyah, et al.
Pubblicazione: (2024)

MAFM^3: Modular Adaptation of Foundation Models for Multi-Modal Medical AI
di: Qazi, Mohammad Areeb, et al.
Pubblicazione: (2025)

Forget-MI: Machine Unlearning for Forgetting Multimodal Information in Healthcare Settings
di: Hardan, Shahad, et al.
Pubblicazione: (2025)

UNICON: UNIfied CONtinual Learning for Medical Foundational Models
di: Qazi, Mohammad Areeb, et al.
Pubblicazione: (2025)

ConDiSR: Contrastive Disentanglement and Style Regularization for Single Domain Generalization
di: Matsun, Aleksandr, et al.
Pubblicazione: (2024)

In-Model Merging for Enhancing the Robustness of Medical Imaging Classification Models
di: Wang, Hu, et al.
Pubblicazione: (2025)

Lost in Volume: The CT-SpatialVQA Benchmark for Evaluating Semantic-Spatial Understanding of 3D Medical Vision-Language Models
di: Monon, Mashrafi, et al.
Pubblicazione: (2026)

SimLVSeg: Simplifying Left Ventricular Segmentation in 2D+Time Echocardiograms with Self- and Weakly-Supervised Learning
di: Maani, Fadillah, et al.
Pubblicazione: (2023)

PEMMA: Parameter-Efficient Multi-Modal Adaptation for Medical Image Segmentation
di: Saadi, Nada, et al.
Pubblicazione: (2024)

On Enhancing Brain Tumor Segmentation Across Diverse Populations with Convolutional Neural Networks
di: Maani, Fadillah, et al.
Pubblicazione: (2024)

HuLP: Human-in-the-Loop for Prognosis
di: Ridzuan, Muhammad, et al.
Pubblicazione: (2024)

DARK: Diagonal-Anchored Repulsive Knowledge Distillation for Vision-Language Models under Extreme Compression
di: Saeed, Numan, et al.
Pubblicazione: (2026)

Efficient Parameter Adaptation for Multi-Modal Medical Image Segmentation and Prognosis
di: Saeed, Numan, et al.
Pubblicazione: (2025)

Continual Learning in Medical Imaging: A Survey and Practical Analysis
di: Qazi, Mohammad Areeb, et al.
Pubblicazione: (2024)

DuPLUS: Dual-Prompt Vision-Language Framework for Universal Medical Image Segmentation and Prognosis
di: Saeed, Numan, et al.
Pubblicazione: (2025)

On Evaluating Adversarial Robustness of Volumetric Medical Segmentation Models
di: Malik, Hashmat Shadab, et al.
Pubblicazione: (2024)

EDUE: Expert Disagreement-Guided One-Pass Uncertainty Estimation for Medical Image Segmentation
di: Abutalip, Kudaibergen, et al.
Pubblicazione: (2024)

SurvRNC: Learning Ordered Representations for Survival Prediction using Rank-N-Contrast
di: Saeed, Numan, et al.
Pubblicazione: (2024)

Advanced Tumor Segmentation in Medical Imaging: An Ensemble Approach for BraTS 2023 Adult Glioma and Pediatric Tumor Tasks
di: Maani, Fadillah, et al.
Pubblicazione: (2024)

MMRINet: Efficient Mamba-Based Segmentation with Dual-Path Refinement for Low-Resource MRI Analysis
di: Elsayed, Abdelrahman, et al.
Pubblicazione: (2025)

FetalCLIP: A Visual-Language Foundation Model for Fetal Ultrasound Image Analysis
di: Maani, Fadillah, et al.
Pubblicazione: (2025)

On the Robustness of Medical Vision-Language Models: Are they Truly Generalizable?
di: Imam, Raza, et al.
Pubblicazione: (2025)

EchoApex: A General-Purpose Vision Foundation Model for Echocardiography
di: Amadou, Abdoul Aziz, et al.
Pubblicazione: (2024)

VA-Adapter: Adapting Ultrasound Foundation Model to Echocardiography Probe Guidance
di: Wang, Teng, et al.
Pubblicazione: (2025)

DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation
di: Rahman, Umaima, et al.
Pubblicazione: (2025)

FUSC: Fetal Ultrasound Semantic Clustering of Second Trimester Scans Using Deep Self-supervised Learning
di: Alasmawi, Hussain, et al.
Pubblicazione: (2023)

Envisioning MedCLIP: A Deep Dive into Explainability for Medical Vision-Language Models
di: Hashmi, Anees Ur Rehman, et al.
Pubblicazione: (2024)

EchoJEPA: A Latent Predictive Foundation Model for Echocardiography
di: Munim, Alif, et al.
Pubblicazione: (2026)

PrivLEX: Detecting legal concepts in images through Vision-Language Models
di: Baranouskaya, Darya, et al.
Pubblicazione: (2026)

Cryo-Bench: Benchmarking Foundation Models for Cryosphere Applications
di: Kaushik, Saurabh, et al.
Pubblicazione: (2026)

DynaMMo: Dynamic Model Merging for Efficient Class Incremental Learning for Medical Images
di: Qazi, Mohammad Areeb, et al.
Pubblicazione: (2024)

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift
di: Rahman, Umaima, et al.
Pubblicazione: (2025)

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation
di: Zhang, Ziyu, et al.
Pubblicazione: (2025)

Advancing Fetal Ultrasound Image Quality Assessment in Low-Resource Settings
di: He, Dongli, et al.
Pubblicazione: (2025)

T3: Test-Time Model Merging in VLMs for Zero-Shot Medical Imaging Analysis
di: Imam, Raza, et al.
Pubblicazione: (2025)

MedMerge: Merging Models for Effective Transfer Learning to Medical Imaging Tasks
di: Almakky, Ibrahim, et al.
Pubblicazione: (2024)

Transesophageal Echocardiography Generation using Anatomical Models
di: Oladokun, Emmanuel, et al.
Pubblicazione: (2024)